From 22bfac190c81f4310f4120b5faf43bae208d0dd8 Mon Sep 17 00:00:00 2001
From: ro7m <robin.mundra@gmail.com>
Date: Sun, 15 Apr 2018 11:56:03 +0530
Subject: [PATCH 1/3] fix broken links

---
 docs/streams/core-concepts.html               | 2 +-
 docs/streams/developer-guide/memory-mgmt.html | 6 ++----
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/docs/streams/core-concepts.html b/docs/streams/core-concepts.html
index 889fe06c45fff..25339339ed66f 100644
--- a/docs/streams/core-concepts.html
+++ b/docs/streams/core-concepts.html
@@ -63,7 +63,7 @@ <h3><a id="streams_topology" href="#streams_topology">Stream Processing Topology
     <ul>
         <li>A <b>stream</b> is the most important abstraction provided by Kafka Streams: it represents an unbounded, continuously updating data set. A stream is an ordered, replayable, and fault-tolerant sequence of immutable data records, where a <b>data record</b> is defined as a key-value pair.</li>
         <li>A <b>stream processing application</b> is any program that makes use of the Kafka Streams library. It defines its computational logic through one or more <b>processor topologies</b>, where a processor topology is a graph of stream processors (nodes) that are connected by streams (edges).</li>
-        <li>A <b>stream processor</b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
+        <li>A <b><a id="stream_processor" href="#streams_processor_node">stream processor</a></b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
     </ul>
 
     There are two special processors in the topology:
diff --git a/docs/streams/developer-guide/memory-mgmt.html b/docs/streams/developer-guide/memory-mgmt.html
index 6c6fd2f76ccb2..18c555deb1571 100644
--- a/docs/streams/developer-guide/memory-mgmt.html
+++ b/docs/streams/developer-guide/memory-mgmt.html
@@ -54,10 +54,8 @@
       </ul>
       <p>For such <code class="docutils literal"><span class="pre">KTable</span></code> instances, the record cache is used for:</p>
       <ul class="simple">
-        <li>Internal caching and compacting of output records before they are written by the underlying stateful
-          <a class="reference internal" href="../concepts.html#streams-concepts-processor"><span class="std std-ref">processor node</span></a> to its internal state stores.</li>
-        <li>Internal caching and compacting of output records before they are forwarded from the underlying stateful
-          <a class="reference internal" href="../concepts.html#streams-concepts-processor"><span class="std std-ref">processor node</span></a> to any of its downstream processor nodes.</li>
+        <li>Internal caching and compacting of output records before they are written by the underlying stateful processor node to its internal state stores.</li>
+        <li>Internal caching and compacting of output records before they are forwarded from the underlying stateful processor node to any of its downstream processor nodes.</li>
       </ul>
       <p>Use the following example to understand the behaviors with and without record caching. In this example, the input is a
         <code class="docutils literal"><span class="pre">KStream&lt;String,</span> <span class="pre">Integer&gt;</span></code> with the records <code class="docutils literal"><span class="pre">&lt;K,V&gt;:</span> <span class="pre">&lt;A,</span> <span class="pre">1&gt;,</span> <span class="pre">&lt;D,</span> <span class="pre">5&gt;,</span> <span class="pre">&lt;A,</span> <span class="pre">20&gt;,</span> <span class="pre">&lt;A,</span> <span class="pre">300&gt;</span></code>. The focus in this example is

From b577238c0d99163885ec9975931bad721610e36f Mon Sep 17 00:00:00 2001
From: ro7m <robin.mundra@gmail.com>
Date: Sun, 15 Apr 2018 12:02:07 +0530
Subject: [PATCH 2/3] fix broken links

---
 docs/streams/developer-guide/memory-mgmt.html | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/streams/developer-guide/memory-mgmt.html b/docs/streams/developer-guide/memory-mgmt.html
index 18c555deb1571..a73a814be116f 100644
--- a/docs/streams/developer-guide/memory-mgmt.html
+++ b/docs/streams/developer-guide/memory-mgmt.html
@@ -54,8 +54,10 @@
       </ul>
       <p>For such <code class="docutils literal"><span class="pre">KTable</span></code> instances, the record cache is used for:</p>
       <ul class="simple">
-        <li>Internal caching and compacting of output records before they are written by the underlying stateful processor node to its internal state stores.</li>
-        <li>Internal caching and compacting of output records before they are forwarded from the underlying stateful processor node to any of its downstream processor nodes.</li>
+        <li>Internal caching and compacting of output records before they are written by the underlying stateful
+          <a class="reference internal" href="../core-concepts#streams_processor_node"><span class="std std-ref">processor node</span></a> to its internal state stores.</li>
+        <li>Internal caching and compacting of output records before they are forwarded from the underlying stateful
+          <a class="reference internal" href="../core-concepts#streams_processor_node"><span class="std std-ref">processor node</span></a> to any of its downstream processor nodes.</li>
       </ul>
       <p>Use the following example to understand the behaviors with and without record caching. In this example, the input is a
         <code class="docutils literal"><span class="pre">KStream&lt;String,</span> <span class="pre">Integer&gt;</span></code> with the records <code class="docutils literal"><span class="pre">&lt;K,V&gt;:</span> <span class="pre">&lt;A,</span> <span class="pre">1&gt;,</span> <span class="pre">&lt;D,</span> <span class="pre">5&gt;,</span> <span class="pre">&lt;A,</span> <span class="pre">20&gt;,</span> <span class="pre">&lt;A,</span> <span class="pre">300&gt;</span></code>. The focus in this example is

From 7c0837bff4764aa1ec210224876ef947ee05fe5f Mon Sep 17 00:00:00 2001
From: ro7m <robin.mundra@gmail.com>
Date: Sun, 15 Apr 2018 12:05:49 +0530
Subject: [PATCH 3/3] fix broken links

---
 docs/streams/core-concepts.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streams/core-concepts.html b/docs/streams/core-concepts.html
index 25339339ed66f..16502879d6b74 100644
--- a/docs/streams/core-concepts.html
+++ b/docs/streams/core-concepts.html
@@ -63,7 +63,7 @@ <h3><a id="streams_topology" href="#streams_topology">Stream Processing Topology
     <ul>
         <li>A <b>stream</b> is the most important abstraction provided by Kafka Streams: it represents an unbounded, continuously updating data set. A stream is an ordered, replayable, and fault-tolerant sequence of immutable data records, where a <b>data record</b> is defined as a key-value pair.</li>
         <li>A <b>stream processing application</b> is any program that makes use of the Kafka Streams library. It defines its computational logic through one or more <b>processor topologies</b>, where a processor topology is a graph of stream processors (nodes) that are connected by streams (edges).</li>
-        <li>A <b><a id="stream_processor" href="#streams_processor_node">stream processor</a></b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
+        <li>A <b><a href="#streams_processor_node">stream processor</a></b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
     </ul>
 
     There are two special processors in the topology: